How to derive a Gibbs sampling routine in general

This video illustrates how to derive a Gibbs sampling scheme for an applied example.
This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: www.amazon.co.uk/Students-Gui...
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: • A Student's Guide to B...

[@Andrew6James]

Fantastically clear concise explanation that some textbooks seem to skip over!

[@pradiptahafid]

Hi Ben, Thank you for posting the video. I may be count as 1 count or 3 counts at most to this video but you don't know how much this video has helped me. Thank you!

[@samk5056]

Thank you very much for the detailed explanation 👍🏼 it helps a lot 🙏🏼

[@samk5056]

Thank you very much for detailed explanation 👍🏼 it helps a lot 🙏🏼

[@danielpit8693]

the best tutorial ever!

[@KarinSayWhat]

This is fantastic.

[@AhmedIsam]

4:08 infer n and THETA. not k

[@lemyul]

thanks lamb

[@youtubeadventurer1881]

Can anyone clarify whether each update of one of one of the parameters counts as a new sample, or whether we have to update all parameters before we get a new sample?

[@mynameisjain]

I think a sample consists of a value derived for all parameters. So to get a new sample, all params should be updated. I am not an expert though

[@mohammedhelal5778]

Great stuff Ben! Only thing that would have made it that much better would be code for the sampling process.
In any case thanks a lot for putting these videos together!

[@deepraghosh7157]

What is the modification for Gibbs sampling not requiring conditional distributions that you said towards the end of video?

[@Dupamine]

hamiltonian

[@sumalibajaj4665]

Slice sampling